Robust automatic segmentation of various anatomical structures in a medical image is a key enabler in improving clinical workflows. Here, the term segmentation refers to the identification of the anatomical structure in the medical image by, e.g., delineation of the boundaries of the anatomical structure, or by labeling of the voxels enclosed by the boundaries. Once such segmentation has been performed, it is possible to extract clinical parameters such as, in case of a cardiac structure, ventricular mass, ejection fraction and wall thickness. Consequently, automatic segmentation can significantly reduce the scan-to-diagnosis time, and thus help clinicians in establishing more efficient patient management.
It is known to segment an anatomical structure in a medical image using a deformable model. Such type of segmentation is also referred to as model-based segmentation. The deformable model may be defined by model data. In particular, the model data may define a geometry of the anatomical structure, e.g., in the form of a multi-compartmental mesh of triangles. Inter-patient and inter-phase shape variability may be efficiently accounted for by assigning an affine transformation to each part of such a deformable model. Affine transformations cover translation, rotation, scaling along different coordinate axes and shearing. Moreover, mesh regularity may be maintained by interpolation of the affine transformations at the transitions between different parts of the deformable model. It is noted that such deformable models are also referred to as mean shape models.
The fitting or applying of a deformable model to the image data of the medical image, also referred to as mesh adaptation, may involve optimizing an energy function which may be based on an external energy term which helps to adapt the deformable model to the image data and an internal energy term which maintains a rigidness of the deformable model. It is noted that such an external energy term might make use of boundary detection functions that were trained during a so-termed simulated search, and may model different image characteristics inherent to different medical imaging modalities and/or protocols.
Deformable models of the above described type are known per se, as are methods of applying such models to an anatomical structure in a medical image.
For example, a publication titled “Automatic Model-based Segmentation of the Heart in CT Images” by O. Ecabert et al., IEEE Transactions on Medical Imaging 2008, 27(9), pp. 1189-1201, describes a model-based approach for the automatic segmentation of the heart (four chambers, myocardium, and great vessels) from three-dimensional (3D) Computed Tomography (CT) images. Here, model adaptation is performed progressively increasing the degrees-of-freedom of the allowed deformations to improve convergence as well as segmentation accuracy. The heart is first localized in the image using a 3D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multi-compartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy.